生物多样性 ›› 2017, Vol. 25 ›› Issue (8): 799-806.DOI: 10.17520/biods.2015218

• 研究报告: 植物多样性 • 上一篇    下一篇

濒危物种长柄扁桃的潜在分布与保护策略

褚建民1, 李毅夫2, 张雷1, 李斌1, 高明远1, 唐晓倩1, 倪建伟1, 许新桥1,*()   

  1. 1 (中国林业科学研究院林业研究所国家林业局林木培育重点实验室, 北京 100091)
    2 (西南林业大学林学院, 昆明 650224);
  • 收稿日期:2016-11-22 接受日期:2017-02-17 出版日期:2017-08-20 发布日期:2017-08-31
  • 通讯作者: 许新桥
  • 作者简介:具体评估过程包括信息汇总(各个渠道的标本信息、野外调查信息及文献资料)、逐条比对IUCN红色名录等级与标准、确定等级、填写评估说明。在具体评估过程中, 针对不同类群设计信息调查表, 通过电话和邮件向多位同行征询物种的居群信息。
  • 基金资助:
    林业公益性行业专项(201204307)、科技基础性工作专项(2013FY1l1700)和国家自然科学基金(41301056)

Potential distribution range and conservation strategies for the endangered species Amygdalus pedunculata

Jianmin Chu1, Yifu Li2, Lei Zhang1, Bin Li1, Mingyuan Gao1, Xiaoqian Tang1, Jianwei Ni1, Xinqiao Xu1,*()   

  1. 1 Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091
    2 College of Forestry, Southwest Forestry University, Kunming 650224
  • Received:2016-11-22 Accepted:2017-02-17 Online:2017-08-20 Published:2017-08-31
  • Contact: Xu Xinqiao

摘要:

长柄扁桃(Amygdalus pedunculata)在我国分布于内蒙古和陕西, 是一种濒危灌木, 其资源现状只有零星的文献记录, 这限制了对其资源数量和保护现状的评估。为此, 本文通过野外调查来确立其自然分布区范围。我们选择了8个环境因子, 运用基于规则集的遗传算法(genetic algorithm for rule-set prediction, GARP)模型和最大熵(maximum entropy, MaxEnt)模型进行模拟, 预测了长柄扁桃在中国的潜在适宜分布区; 同时基于4个模型精度评估指标(Kappa、真实技巧统计法、总精度和受试者工作特征曲线下的面积)对模型进行验证, 采用刀切法评估了预测变量的重要性。结果表明, 两种模型均能准确预测长柄扁桃的地理分布规律, 但MaxEnt模型的4个预测精度指标都大于GARP模型。根据模型结果可判断长柄扁桃的适宜分布区以内蒙古中部地区为主, 东起大兴安岭南部, 向西可至贺兰山、乌兰布和沙漠以东, 涵盖了毛乌素沙地、库布齐沙漠和浑善达克沙地, 以及乌拉山、大青山等土石山区。其低适宜分布区可辐射至辽宁、河北、山西、陕西等省部分地区, 另外在宁夏和甘肃中部地区也有零星的低适宜分布区。变量重要性分析结果表明, 与降水相关的变量是决定长柄扁桃地理分布的重要环境因素。

关键词: 长柄扁桃, 潜在分布区, 最大熵模型, 基于规则集的遗传算法(GARP)模型, 物种保护

Abstract:

The endangered shrub species Amygdalus pedunculata is distributed in Inner Mongolia Autonomous Region and Shaanxi Province, China. However, little is known about its resource quantity and conservation status. A field investigation was conducted to determine the natural distribution range of A. pedunculata. Eight environmental factors were chosen to build models with the genetic algorithm for rule-set prediction (GARP) model and maximum entropy (MaxEnt) model. We predicted the potential distribution of A. pedunculata in China. Using four model evaluation metrics (Kappa, true skill statistic (TSS), overall accuracy and area under the receiver operating characteristic curve (AUC)), we assessed the predictive performance of both models. The Jackknife method was used to investigate the most important environmental factors for the distribution of A. pedunculata. The results indicated that both models were effective for predicting the distribution of A. pedunculata, but MaxEnt performed better than GARP in terms of the four evaluation metrics. The species was predicted to have a broad suitable area, which ranged from the south of Daxing’anling to the east of Helan Mountains and the Ulan Buh Desert. Amygdalus pedunculata is mainly distributed in the middle regions of Inner Mongolia, including Mu Us Sandy Land, Kubuqi desert, Otindag Sandy Land, and the Wulashan and Daqingshan Mountains. Low suitable sites occurred in some regions of Liaoning, Hebei, Shanxi and Shaanxi. Besides, and there were some sporadic low suitable areas in the middle regions of the Ningxia Hui Autonomous Region and Gansu Province. Variable importance analysis showed that the variables relevant to precipitation had more significant effects on the geographic distribution of A. pedunculata. These results have important implications for resource conservation and ecology including the revegetation of semi-arid ecosystems.

Key words: Amygdalus pedunculata, potential distribution area, MaxEnt model, GARP model, species conservation